Digital television transmission commonly relies heavily on powerful image compression techniques being used, to reduce the number of digital symbols that must be transmitted in order to describe sequential moving images. Forward error-correction coding is performed on the digital signals, primarily to combat impulse noise or burst noise. Until the bit-error rate (BER) becomes too great for correction responsive to the forward error-correction coding, the images regenerated from the received digital signals will exhibit little or no discernible deterioration. When the BER exceeds the capability of the error-correcting coding, there is a catastrophic failure in the capability to reconstruct the transmitted image, so the TV receiver cannot reproduce new picture information. Arrangements can be made to present on the viewing screen a frozen picture, corresponding to the last television image satisfactorily received or to an extrapolation from the last few television images satisfactorily received. There will be no audio accompanying the frozen picture.
Since the image regeneration process is essentially perfect until the BER overwhelms forward error-correction coding capability, and since there is virtually complete failure in the image regeneration process thereafter, it is difficult to characterize the visual effects of multipath or or other channel distortions on a digital television receiver. Accordingly, the effects of multipath distortion on a digital television receiver are normally characterized in terms of the rate of error in digital symbols before error correction is implemented. It is very desirable to compensate against multipath distortion, to the extent that it causes significant increase in the rate of error in digital symbols before error correction is implemented. This helps avoid the occurrence of failure in the image regeneration process caused by forward error-correction coding capability being overwhelmed.
The transmission channel in which multipath distortion arises can be characterized as a tapped-delay-line filter responding to the originally transmitted signal in accordance with a first transfer characteristic. The output port of this filter, which is in the digital radio receiver, is commonly assumed to be at the output port of the final detector for carrier modulation, prior to re-digitization of the detected signal. The effects of multipath distortion on the response of this filter can be compensated for at the digital receiver by passing the digitized response with multipath distortion through a channel-equalization filter having a second transfer characteristic that when multiplied by the first transfer characteristic generates a product that through a frequency band of interest has substantially uniform group delay and a substantially flat amplitude-versus-frequency characteristic. Since multipath distortion is liable to change from time to time within a transmission channel, and since the multipath distortion characteristics of different transmission channels differ in a radio receiver capable of selecting amongst different transmission channels, the channel-equalization filter is usually an adaptive filter, the filtering characteristics of which can be adjusted responsive to reception conditions.
Since the characteristics of a transmitted digital signal are known a priori, it is possible, at least in theory, to utilize such characteristics in a system of multipath detection and adaptive channel equalization. Various problems limit this approach to channel equalization, however. Accordingly, television engineers have found it desirable to transmit recurrently a training signal situated in a portion of the TV signal that is currently unused for video purposes and to utilize this training signal for the detection and characterization of multipath distortion prior to arranging for its suppression. Such a signal is herein referred to as a training signal; and a variety of different training or "ghost cancelation reference" signals have been described in patents and other technical publications. The strategy for eliminating multipath distortion relies on the transmitted training signal suffering the same multipath distortions as the rest of the television signal. A computer in the receiver can then examine the distorted training signal that is received and, with a priori knowledge of the distortion-free training signal, can calculate the characteristics of the transmission channel. The computer can then calculate the characteristics required of a filter that will respond to the received signal, but will suppress the effects of multipath signals.
In the digital television signals for broadcasting high-definition television (HDTV), each data field contains 313 data lines, and the fields are consecutively numbered modulo-two in order of their occurrence. Each line of data starts with a line synchronization code group of four symbols having successive values of +S, -S, -S and +S. The value +S is one level below the maximum positive data excursion, and the value -S is one level above the maximum negative data excursion. The lines of data are each of 77.3 microsecond duration, and there are 832 symbols per data line for a symbol rate of about 10 megabits/second. The initial line of each data field is a field synchronization code group that codes a training signal for channel-equalization and multipath signal suppression procedures. The training signal is a 511-sample pseudo-random sequence (or "PR-sequence") followed by three 63-sample PR sequences. This training signal is transmitted in accordance with a first logic convention in the first line of each odd-numbered data field and in accordance with a second logic convention in the first line of each even-numbered data field, the first and second logic conventions being one's complementary respective to each other. The reference sequence(s) can be analyzed, channel characterization determined and appropriate equalizing filter can be implemented. However, this process can be rather slow and is definitely not suitable for any multipath signal, such as some airplane flutter, that varies quite quickly with elapsed time.
Owing to the nature of the digital signal used in HDTV, the adaptation of the channel-equalization filter could be performed with every received symbol on a decision-directed basis (in the absence of the reference sequence). However, currently the limiting factor on the speed of initially equalizing the reception channel or of tracking a time-varying multipath is established by the processing speeds of the computing devices being utilized. Increasing the processing speeds of the computing devices will improve system performance until the point is reached at which all the computations and the subsequent updating of the filter coefficients can be realized with each newly received symbol or with a reasonably small group of newly received symbols.
Several methods of performing "adaptive equalization/multipath cancellation" are described in the literature. In simplest terms, the input signal is processed through an equalizer filter. The filter output, is "compared" to the desired output and based on a certain algorithm a correction to the filter parameters is computed and adapted to the filter. The process is continuously repeated until the equalized filter output is "correct", so multipath effects are attenuated sufficiently that they do not exceed levels prescribed as being "acceptable". To aid in developing an understanding of the nature of the computations involved, the reader is referred to the following publications, incorporated by reference:
G. A. Clark, S. K. Mitra, S. R. Parker, "Block implementation of adaptive digital filters," IEEE Trans. ASSP, pp. 744-752, Vol. 29, June 1981, and PA0 J. C. Lee and C. K. Un, "Performance Analysis of Frequency-Domain Block LMS Adaptive Digital Filters," IEEE Trans. on Circuits and Systems, pp. 173-189, Vol. 36, No. 2, Feb. 1989.
The basic adaptive equalization/multipath cancellation equations are known from the last-listed of these references to be: ##EQU1## This adaptation algorithm is based on a group of N symbols and not on each symbol. Such an algorithm is identified as "Block LMS". It is known to have the same performance as the well-known LMS (least mean squares) algorithm when the channel varying speed is slower than the realized convergence with the block of N symbols. (Superscripted terms in these equations are not terms raised to "powers" indicated by the superscript. Rather the superscripts following general terms are a set of further indices for sets of specific terms, the specific terms in each set being indexed by subscripts following general terms.) A channel-equalization filter with coefficients W.sub.k (the parameter m is not shown here since it only indicates the number of updates) and input data x.sup.n (ghosted and/or equalization needed) generates equalized data y.sup.n according to equation (1). Since the equalization indicated by equation (1) must be done in real-time, standard practice is to implement that equalization using an appropriate FIR filter. When equalization is done using a training signal, an IIR filter suppresses multipath responses that are delayed respective to strongest signal better than an FIR filter having the same number of taps. In decision-directed equalization, the computation of weighting coefficients for the channel-equalization filter is based strictly on some observation that does not depend on or indicate the time relationship of multipath signals. When the computation procedure begins without knowledge of suitable initial values of the weighting coefficients, the procedure is referred to as "blind" equalization. Because the response of an IIR filter is regenerative in nature, errors introduced by "blind" equalization tend to be perpetuated and will be rarely eliminated by continuing calculation. Presumably this is the reason that, until the invention described in this specification was made, decision-directed equalization had invariably been used only with FIR channel-equalization filters.
Until the invention described in this specification was made, the computation for the filter adaptation has been performed using a type of microprocessor commonly known as a "digital signal processor" or "DSP". For each sample data y.sup.n, an estimate of error e.sup.n is computed from the known or expected (decision directed) value of y.sup.n according to equation (2). The error estimate and input data x.sup.n are used to compute the correction for the equalizing filter coefficients W.sup.k according to equation (3). Then the coefficients W.sup.k are updated using this correction. The parameter m in the equation (4) indicates the corrections sequence.
Since the amount of correction to the coefficients can be in error, depending upon the incoming data and on the estimated value of y, it is prudent to use only a fraction of the predicted correction in which case the convergence to the correct set of coefficients W.sup.k will be slow. However, if there was an error in prediction, its effect on the result will be minimal. It may be desired to compute and implement correction from every set of data. However, the rate of incoming data is about 10 megasymbols per second for the Grand Alliance system using vestigial sideband (VSB) transmission and is about 5 megasymbols per second for the General Instrument cable-HDTV system using quadrature-amplitude-modulation (QAM) transmission sometimes referred to as "complex amplitude modulation transmission". However, it should be pointed out that in case of QAM data x.sup.n, y.sup.n, etc. are complex, so the the term x.sup.(j-k) in equation (3) will be the complex conjugate, x.sup.(j-k) *. Considering the speed of commercially available DSP microprocessors, computing and implementing correction from every set of data is impractical.
To implement this process using a training signal, it is a general practice to store the known training signal in a read-only memory (ROM) and to use a DSP (microprocessor) to compute W.sup.k and update the equalizing filter coefficients. Thus, the rate at which the equalization can be realized is based on the operating speed of the DSP and the processing time to compute the W.sub.k. For example equation (3) takes N multiply-add operations per update (or about 2.5.multidot.10.sup.12 multiply-add operations per update for N=256 and a data rate of 10 megasymbols/second). This is far beyond what a microprocessor can handle. As a matter of fact, even the fastest microprocessor limits the rate of equalizing filter coefficients update, since the training signal length and the amount of computations required are huge. Even the decision-directed computation is slow, since the time required to compute the correction is relatively large for the available DSP speed. This handicap directly reflects in the limitation of handling the time-varying multipaths.